FFT_cal

BlurTemplate.m为模糊模板函数子程序 DIPexp2.m为用逆滤波和维纳滤波进行恢复的源程序(Template function for the fuzzy BlurTemplate.m Subroutine DIPexp2.m using inverse filtering and Wiener filtering for the restoration of the source)

利用sift算法提取两张图像的特征点，利用ransac去除误匹配，最后将两张图片拼接在一起(Using sift algorithm to extract the two feature points of the image, the use of ransac to remove the wrong match, and finally the two pictures together)

针对散焦模糊图像，用matlab实现了先基于倒谱相关性对PSF的参数估计，再用维纳滤波实现复原的算法。(Defocus blurred image using matlab first estimated based on the parameters of the cepstrum on PSF, and then the Wiener filter recovery algorithm.)

运用matlab提取视频，显示出视频图像的图像特征，并以柱状图形式显示(Use matlab extract video shows the image feature of the video image, and is displayed in histogram form)

卷积神经网络深度学习，可以识别多种图像，并自动解出所需要的结果(Deep learning of convolutional neural networks)

图像的中值滤波源码例程程序结合易语言数值计算支持库和图像格式转换支持库，调用正态分布图像处理模块实现图像的中值滤波功能。(Image median filtering routine program combined with easy language source numerical support libraries and image format conversion support library , call the normal image processing module median filtering images.)

外国人写的图像融合处理工具以及GUI界面 源代码 相信会对你有所帮助(foreigners write image-processing tools and the integration of the GUI interface believe that the source code will help you)

基于matlab实现干涉显微镜图像重构中的二维解包裹函数。( QualityGuidedUnwrap2D implements 2D quality guided path following phase unwrapping algorithm. Inputs: 1. Complex image in .mat double format 2. Binary mask (optional) Outputs: 1. Unwrapped phase image 2. Phase quality map This code can easily be extended for 3D phase unwrapping. Technique adapted from: D. C. Ghiglia and M. D. Pritt, Two-Dimensional Phase Unwrapping: Theory, Algorithms and Software. New York: Wiley-Interscience, 1998. Posted by Bruce Spottiswoode on 22 December 2008 2010/07/23 Modified by Carey Smith)

西安科技大学，图像处理教程的课件，做成了PDF格式，希望对你们有用(Xi' an University of Technology, image processing tutorial courseware, made the PDF format, hope that is helpful for you)

说明：  利用97小波变换进行图像压缩，用matlab仿真，实验证明它具有较高的压缩比(using wavelet transform image compression using Matlab simulation experiment to prove that it has a higher compression ratio)